1 Load environment

Code

import warnings

warnings.filterwarnings("ignore")

import matplotlib.pyplot as plt
import seaborn as sns
import scanpy as sc
import pandas as pd
import numpy as np
import random
import itertools

from tqdm import tqdm

import decoupler as dc
import sys

sys.path.append("./../../../../utilities_folder")
from utilities import load_object, intTable, plotGenesInTerm, getAnnGenes, run_ora_catchErrors

Set R environment with rpy2:

Code

import rpy2.rinterface_lib.callbacks
import anndata2ri
import logging

from rpy2.robjects import pandas2ri
import rpy2.robjects as ro

sc.settings.verbosity = 0
rpy2.rinterface_lib.callbacks.logger.setLevel(logging.ERROR)

pandas2ri.activate()
anndata2ri.activate()

%load_ext rpy2.ipython

Set up of graphical parameters for Python plots:

Code

%matplotlib inline
sc.set_figure_params(dpi = 300, fontsize = 20)

cmap_up = sns.light_palette("red", as_cmap=True)
cmap_down = sns.light_palette("blue", as_cmap=True)
cmap_all = sns.light_palette("seagreen", as_cmap=True)

Set up of graphical parameters for R plots:

Code

default_units = 'in' 
default_res = 300
default_width = 10
default_height = 9

import rpy2
old_setup_graphics = rpy2.ipython.rmagic.RMagics.setup_graphics

def new_setup_graphics(self, args):
    if getattr(args, 'units') is not None:
        if args.units != default_units:
            return old_setup_graphics(self, args)
    args.units = default_units
    if getattr(args, 'res') is None:
        args.res = default_res
    if getattr(args, 'width') is None:
        args.width = default_width
    if getattr(args, 'height') is None:
        args.height = default_height        
    return old_setup_graphics(self, args)


rpy2.ipython.rmagic.RMagics.setup_graphics = new_setup_graphics

Here the cell were we inject the parameters using Quarto renderer:

Code

# Injected Parameters
N = 3

Code

# Injected Parameters
N = 7

Import R libraries:

Code

%%R
source('./../../../../utilities_folder/GO_helper.r')
loc <- './../../../../R_loc' # pointing to the renv environment

.libPaths(loc)

library('topGO')
library('org.Hs.eg.db')
library(dplyr)
library(ggplot2)

Set output folders:

Code

output_folder = './'
folder = './deg_in_cluster_tables/cluster_' + str(N) + '/'

import os

if not os.path.isdir(folder):
    os.makedirs(folder)

2 Load data

Here we load the anndata:

Code

GO2gene = load_object('./../../../../data/GO2gene_complete.pickle')

Here we load the dictionary that associates to each GO term its genes:

Code

adata = sc.read("1_All_Annotated_Triku.h5ad")
adata

AnnData object with n_obs × n_vars = 27925 × 14582
    obs: 'sample_id', 'run_id', 'probe_barcode_ids', 'subject', 'line', 'specimen', 'stage', 'condition', 'notes', 'seqRun', 'original_name', 'project', 'who', 'SC_derivation', 'micoplasma', 'mosaic', 'genSite', 'n_genes_by_counts', 'log1p_n_genes_by_counts', 'total_counts', 'log1p_total_counts', 'total_counts_mito', 'log1p_total_counts_mito', 'pct_counts_mito', 'total_counts_ribo', 'log1p_total_counts_ribo', 'pct_counts_ribo', 'gene_UMI_ratio', 'log1p_gene_UMI_ratio', 'scDblFinder_score', 'scDblFinder_class', 'n_counts', 'n_genes', 'leiden', 'score_pluripotency', 'score_mesoderm', 'score_PS', 'score_EM', 'score_AM', 'score_Endo', 'score_Epi', 'score_HEP', 'score_NM', 'score_ExM', 'score_Ery', 'score_EAE', 'score_Amnion_LC', 'score_AxM', 'score_Amnion', 'score_NNE', 'score_PGC', 'score_hPGCLCs', 'score_DE1', 'score_DE2', 'score_Hypoblast', 'score_YS', 'score_EMPs', 'score_Endothelium', 'score_Myeloid_Progenitors', 'score_Megakaryocyte_Erythroid_progenitors', 'CellTypes'
    var: 'n_cells', 'mito', 'ribo', 'n_cells_by_counts', 'mean_counts', 'log1p_mean_counts', 'pct_dropout_by_counts', 'total_counts', 'log1p_total_counts', 'highly_variable', 'means', 'dispersions', 'dispersions_norm', 'highly_variable_nbatches', 'highly_variable_intersection', 'triku_distance', 'triku_distance_uncorrected', 'triku_highly_variable'
    uns: 'CellTypes_colors', 'dendrogram_CellTypes', 'dendrogram_leiden', 'dendrogram_specimen', 'hvg', 'leiden', 'leiden_colors', 'log1p', 'neighbors', 'pca', 'rank_genes_groups', 'run_id_colors', 'sample_id_colors', 'specimen_colors', 'stage_colors', 'triku_params', 'umap'
    obsm: 'X_pca', 'X_pca_harmony', 'X_umap'
    varm: 'PCs'
    layers: 'raw'
    obsp: 'connectivities', 'distances'

3 Markers of cluster

We filter genes for the cluster under investigation based on the p-value adjusted that we then convert in -log(p-value adjusted):

Code

markers = sc.get.rank_genes_groups_df(adata, group=str(N))
intTable(markers, folder=folder, fileName = 'markers_cluster_before_filtering_' + str(N) + '.xlsx', save = True)

Code

markers = markers.set_index('names')
markers = markers[markers.pvals_adj < 0.01]
markers['FDR'] = markers['pvals_adj']
markers['-log10(FDR)'] = -np.log10(markers.pvals_adj)
markers = markers.replace(np.inf, markers[markers['-log10(FDR)'] != np.inf]['-log10(FDR)'].max())
markers

	scores	logfoldchanges	pvals	pvals_adj	pct_nz_group	pct_nz_reference	FDR	-log10(FDR)
names
AASS	33.710232	1.944690	4.093320e-249	5.968879e-245	1.000000	0.838846	5.968879e-245	244.224107
ZIC5	33.446999	2.003957	2.845883e-245	2.074933e-241	0.966617	0.590322	2.074933e-241	240.682996
SALL2	33.020630	1.548278	4.108356e-239	1.996935e-235	0.995549	0.834782	1.996935e-235	234.699636
COX7C	30.139898	0.793442	1.454969e-199	5.304089e-196	0.997033	0.987847	5.304089e-196	195.275389
IGF2BP3	29.930126	0.927935	7.982601e-197	2.328046e-193	0.996291	0.958648	2.328046e-193	192.633009
...	...	...	...	...	...	...	...	...
COL1A1	-27.522104	-3.247142	9.549792e-167	7.329214e-164	0.556380	0.782368	7.329214e-164	163.134943
COL6A2	-28.447588	-2.236870	5.219771e-178	5.854977e-175	0.833828	0.931106	5.854977e-175	174.232475
RHOB	-28.617102	-2.405459	4.116757e-180	5.457323e-177	0.416172	0.717952	5.457323e-177	176.263020
S100A10	-29.503042	-1.331147	2.631525e-191	4.796613e-188	0.908012	0.958648	4.796613e-188	187.319065
VIM	-29.739084	-1.849072	2.400204e-194	4.999969e-191	0.916172	0.964631	4.999969e-191	190.301033

7436 rows × 8 columns

Code

intTable(markers, folder=folder, fileName = 'markers_cluster_filtered_' + str(N) + '.xlsx', save = True)

3.1 Select up and downregulated markers

3.1.1 Up

Code

pos = markers[markers.logfoldchanges > 1]
pos

	scores	logfoldchanges	pvals	pvals_adj	pct_nz_group	pct_nz_reference	FDR	-log10(FDR)
names
AASS	33.710232	1.944690	4.093320e-249	5.968879e-245	1.000000	0.838846	5.968879e-245	244.224107
ZIC5	33.446999	2.003957	2.845883e-245	2.074933e-241	0.966617	0.590322	2.074933e-241	240.682996
SALL2	33.020630	1.548278	4.108356e-239	1.996935e-235	0.995549	0.834782	1.996935e-235	234.699636
USP44	29.769581	1.827461	9.676782e-195	2.351780e-191	0.981454	0.634948	2.351780e-191	190.628603
SFRP1	29.383654	2.131806	8.883629e-190	1.439345e-186	0.988872	0.673101	1.439345e-186	185.841835
...	...	...	...	...	...	...	...	...
ANKRD34B	3.018127	1.287131	2.543422e-03	5.200978e-03	0.088279	0.040260	5.200978e-03	2.283915
HMX2	2.988426	1.470011	2.804184e-03	5.701423e-03	0.079377	0.031644	5.701423e-03	2.244017
DGKB	2.964067	1.316566	3.036022e-03	6.147080e-03	0.086053	0.038943	6.147080e-03	2.211331
MCOLN2	2.885745	1.234584	3.904887e-03	7.786279e-03	0.087537	0.041803	7.786279e-03	2.108670
HTR1F	2.870430	1.654303	4.099138e-03	8.144655e-03	0.070475	0.024495	8.144655e-03	2.089127

270 rows × 8 columns

Code

pos['-log10(FDR)'] = -np.log10(pos.pvals_adj)
#pos = pos.fillna('inf')
pos = pos.replace(np.inf, pos[pos['-log10(FDR)'] != np.inf]['-log10(FDR)'].max())
pos['FDR'] = pos['pvals_adj']
up_regGenes = pos.index.tolist()
allUpSelected = adata.var_names.isin(up_regGenes).astype('int').tolist()

3.1.2 Down

Code

neg = markers[markers.logfoldchanges < -1].sort_values(by = 'logfoldchanges')
neg

	scores	logfoldchanges	pvals	pvals_adj	pct_nz_group	pct_nz_reference	FDR	-log10(FDR)
names
HAND2	-18.468622	-30.753777	3.693894e-76	1.801484e-74	0.000000	0.297701	1.801484e-74	73.744370
ZEB2	-17.607283	-30.237965	2.165965e-69	8.355584e-68	0.000000	0.283817	8.355584e-68	67.078023
PITX1	-16.493843	-29.990402	4.062659e-61	1.199225e-59	0.000000	0.265869	1.199225e-59	58.921099
RSPO3	-13.172199	-29.434788	1.268487e-39	1.787157e-38	0.000000	0.212326	1.787157e-38	37.747837
RGS13	-11.188083	-29.233637	4.664113e-29	4.456887e-28	0.000000	0.180344	4.456887e-28	27.350968
...	...	...	...	...	...	...	...	...
CHST1	-3.378960	-1.003635	7.276060e-04	1.584757e-03	0.067507	0.121233	1.584757e-03	2.800037
MST1	-4.885293	-1.002620	1.032748e-06	2.963309e-06	0.100148	0.177183	2.963309e-06	5.528223
CCDC102A	-7.122588	-1.001501	1.059188e-12	4.613225e-12	0.169139	0.275840	4.613225e-12	11.335995
PPT2	-12.307971	-1.000998	8.206076e-35	9.712744e-34	0.354599	0.510705	9.712744e-34	33.012658
RSPRY1	-16.194717	-1.000380	5.494952e-59	1.511838e-57	0.531157	0.692930	1.511838e-57	56.820495

886 rows × 8 columns

Code

neg['-log10(FDR)'] = -np.log10(neg.pvals_adj)
#neg = neg.fillna('inf')
neg = neg.replace(np.inf, neg[neg['-log10(FDR)'] != np.inf]['-log10(FDR)'].max())
neg['FDR'] = neg['pvals_adj']
down_regGenes = neg.index.tolist()
allDownSelected = adata.var_names.isin(down_regGenes).astype('int').tolist()

3.1.3 All regulated

Code

all_sign = up_regGenes + down_regGenes
allSelected = adata.var_names.isin(all_sign).astype('int').tolist()
allGenes = adata.var_names.tolist()

4 topGO

4.1 Upregulated

Code

%%R -i allUpSelected -i allGenes

allGenes_v <- c(allUpSelected)
#print(allGenes_v)
names(allGenes_v) <- allGenes
allGenes_v <- unlist(allGenes_v)

geneNames <- c(allGenes)

ann_org_BP <- topGO::annFUN.org(whichOnto='BP', feasibleGenes=names(allGenes_v), 
                           mapping='org.Hs.eg', ID='symbol')

ann_org_MF <- topGO::annFUN.org(whichOnto='MF', feasibleGenes=names(allGenes_v), 
                           mapping='org.Hs.eg', ID='symbol')

ann_org_CC <- topGO::annFUN.org(whichOnto='CC', feasibleGenes=names(allGenes_v), 
                           mapping='org.Hs.eg', ID='symbol')

selection <- function(allScores){return (as.logical(allScores))}

Biological Process

Get topGO results:

Code

%%R -o results

GOdata <- new("topGOdata",
  ontology="BP",
  allGenes=allGenes_v,
  annot=annFUN.GO2genes,
  GO2genes=ann_org_BP,
  geneSel = selection,
  nodeSize=10)

results <- runTest(GOdata, algorithm="weight01",statistic="fisher")

Convert results from R environment to Python:

Code

scores = ro.r.score(results)
score_names = ro.r(
'''
names(results@score)
'''
)
go_data = ro.r.GOdata

genesData = ro.r(
'''
geneData(results)
'''
)
genesData

array([13071,   250,    10,  3652], dtype=int32)

Code

#num_summarize = min(100, len(score_names))
results_table = ro.r.GenTable(go_data, weight=results,
        orderBy="weight", topNodes=len(scores))

results_table_py = ro.conversion.rpy2py(results_table)

scores_py = ro.conversion.rpy2py(scores)
score_names = [i for i in score_names]

scores_df = pd.DataFrame({'Scores': scores_py, 'GO.ID': score_names})
results_table_py = results_table_py.merge(scores_df, left_on = 'GO.ID', right_on = 'GO.ID')
results_table_py

	GO.ID	Term	Annotated	Significant	Expected	weight	Scores
0	GO:0051444	negative regulation of ubiquitin-protein...	14	4	0.27	0.00011	0.000112
1	GO:0016338	calcium-independent cell-cell adhesion v...	15	4	0.29	0.00015	0.000151
2	GO:0010001	glial cell differentiation	184	9	3.52	0.00022	0.000221
3	GO:0009410	response to xenobiotic stimulus	328	16	6.27	0.00048	0.000485
4	GO:0019637	organophosphate metabolic process	860	19	16.45	0.00102	0.001020
...	...	...	...	...	...	...	...
6269	GO:2001239	regulation of extrinsic apoptotic signal...	36	0	0.69	1.00000	1.000000
6270	GO:2001240	negative regulation of extrinsic apoptot...	26	0	0.50	1.00000	1.000000
6271	GO:2001241	positive regulation of extrinsic apoptot...	10	0	0.19	1.00000	1.000000
6272	GO:2001258	negative regulation of cation channel ac...	36	0	0.69	1.00000	1.000000
6273	GO:2001267	regulation of cysteine-type endopeptidas...	14	0	0.27	1.00000	1.000000

6274 rows × 7 columns

Filter results:

Code

results_table_py = results_table_py[results_table_py['Scores'] < 0.05]
results_table_py = results_table_py[results_table_py['Annotated'] < 200]
results_table_py = results_table_py[results_table_py['Annotated'] > 15]

results_table_py['-log10(pvalue)'] = - np.log10(results_table_py.Scores)
results_table_py['Significant/Annotated'] = results_table_py['Significant'] / results_table_py['Annotated']

Code

intTable(results_table_py, folder = folder, fileName = 'GO_BP_up.xlsx', save = True)

Code

%%R
Res <- GenTable(GOdata, weight=results,
        orderBy="weight", topNodes=length(score(results)))
#print(Res[0:10,])
colnames(Res) <- c("GO.ID", "Term", "Annotated", "Significant", "Expected", "Statistics")
Res$ER <- Res$Significant / Res$Expected
bubbleplot(Res, Ont = 'BP', fillCol = 'red')

Code

%%R -i markers
plotGenesInTerm_v1(Res, GOdata, SE = markers, nterms=15, ngenes=12,
                             fillCol='red', log = TRUE)

Code

%%R -i markers -i folder
saveGenesInTerm(Res, GOdata, nterms = 20, path = paste0(folder,'GO_BP_genesInTerm_up.xlsx'), SE = markers)

Molecular Function

Code

%%R

GOdata <- new("topGOdata",
  ontology="MF",
  allGenes=allGenes_v,
  annot=annFUN.GO2genes,
  GO2genes=ann_org_MF,
  geneSel = selection,
  nodeSize=10)

Code

%%R -o results

results <- runTest(GOdata, algorithm="weight01",statistic="fisher")

Code

scores = ro.r.score(results)
score_names = ro.r(
'''
names(results@score)
'''
)
go_data = ro.r.GOdata

genesData = ro.r(
'''
geneData(results)
'''
)
genesData

array([13424,   254,    10,   549], dtype=int32)

Code

#num_summarize = min(100, len(score_names))
results_table = ro.r.GenTable(go_data, weight=results,
        orderBy="weight", topNodes=len(scores))

Code

results_table_py = ro.conversion.rpy2py(results_table)

Code

scores_py = ro.conversion.rpy2py(scores)
score_names = [i for i in score_names]

Code

scores_df = pd.DataFrame({'Scores': scores_py, 'GO.ID': score_names})
results_table_py = results_table_py.merge(scores_df, left_on = 'GO.ID', right_on = 'GO.ID')
results_table_py = results_table_py[results_table_py['Scores'] < 0.05]
results_table_py = results_table_py[results_table_py['Annotated'] < 200]
results_table_py = results_table_py[results_table_py['Annotated'] > 15]

intTable(results_table_py, folder = folder, fileName = 'GO_MF_up.xlsx', save = True)

Code

%%R
Res <- GenTable(GOdata, weight=results,
        orderBy="weight", topNodes=length(score(results)))
#print(Res[0:10,])
colnames(Res) <- c("GO.ID", "Term", "Annotated", "Significant", "Expected", "Statistics")
Res$ER <- Res$Significant / Res$Expected
bubbleplot(Res, Ont = 'MF', fillCol = 'red')

Code

%%R -i markers
plotGenesInTerm_v1(Res, GOdata, SE = markers, nterms=15, ngenes=12,
                             fillCol='red', log = TRUE)

Code

%%R -i markers -i folder
saveGenesInTerm(Res, GOdata, nterms = 20, path = paste0(folder,'GO_MF_genesInTerm_up.xlsx'), SE = markers)

Cellular Compartment

Code

%%R

GOdata <- new("topGOdata",
  ontology="CC",
  allGenes=allGenes_v,
  annot=annFUN.GO2genes,
  GO2genes=ann_org_CC,
  geneSel = selection,
  nodeSize=10)

Code

%%R -o results

results <- runTest(GOdata, algorithm="weight01",statistic="fisher")

Code

scores = ro.r.score(results)
score_names = ro.r(
'''
names(results@score)
'''
)
go_data = ro.r.GOdata

genesData = ro.r(
'''
geneData(results)
'''
)
genesData

array([13666,   260,    10,   433], dtype=int32)

Code

#num_summarize = min(100, len(score_names))
results_table = ro.r.GenTable(go_data, weight=results,
        orderBy="weight", topNodes=len(scores))

Code

results_table_py = ro.conversion.rpy2py(results_table)

Code

scores_py = ro.conversion.rpy2py(scores)
score_names = [i for i in score_names]

Code

scores_df = pd.DataFrame({'Scores': scores_py, 'GO.ID': score_names})
results_table_py = results_table_py.merge(scores_df, left_on = 'GO.ID', right_on = 'GO.ID')

Code

results_table_py = results_table_py[results_table_py['Scores'] < 0.05]
results_table_py = results_table_py[results_table_py['Annotated'] < 200]
results_table_py = results_table_py[results_table_py['Annotated'] > 15]

intTable(results_table_py, folder = folder, fileName = 'GO_CC_up.xlsx', save = True)

Code

%%R
Res <- GenTable(GOdata, weight=results,
        orderBy="weight", topNodes=length(score(results)))
#print(Res[0:10,])
colnames(Res) <- c("GO.ID", "Term", "Annotated", "Significant", "Expected", "Statistics")
Res$ER <- Res$Significant / Res$Expected
bubbleplot(Res, Ont = 'CC', fillCol = 'red')

Code

%%R -i markers
plotGenesInTerm_v1(Res, GOdata, SE = markers, nterms=12, ngenes=12,
                             fillCol='red', log = TRUE)

Code

%%R -i markers -i folder
saveGenesInTerm(Res, GOdata, nterms = 20, path = paste0(folder,'GO_CC_genesInTerm_up.xlsx'), SE = markers)

Pathway annotation

4.1.0.1 Reactome

Code

curated = msigdb[msigdb['collection'].isin(['reactome_pathways'])]
curated = curated[~curated.duplicated(['geneset', 'genesymbol'])]

aggregated = curated[["geneset", "genesymbol"]].groupby("geneset").count().rename(columns={"genesymbol": "gene_count"})
curated = curated[~curated.geneset.isin(aggregated[aggregated.gene_count > 200].index.tolist())].copy()
curated = curated[~curated.geneset.isin(aggregated[aggregated.gene_count < 15].index.tolist())].copy()

Code

rank = pd.DataFrame(markers['-log10(FDR)'])

rank_copy = rank.copy()
rank_copy['pval'] = markers.loc[rank.index].FDR

Code

rank_copy

	-log10(FDR)	pval
names
AASS	244.224107	5.968879e-245
ZIC5	240.682996	2.074933e-241
SALL2	234.699636	1.996935e-235
COX7C	195.275389	5.304089e-196
IGF2BP3	192.633009	2.328046e-193
...	...	...
COL1A1	163.134943	7.329214e-164
COL6A2	174.232475	5.854977e-175
RHOB	176.263020	5.457323e-177
S100A10	187.319065	4.796613e-188
VIM	190.301033	4.999969e-191

7436 rows × 2 columns

Code

results_table_py = run_ora_catchErrors(mat=rank.T, net=curated, source='geneset', target='genesymbol', verbose=False, n_up=len(rank), n_bottom=0)
len(results_table_py)

903

Code

intTable(results_table_py, folder = folder, fileName = 'Reactome_up.xlsx', save = True)

Code

if len(results_table_py) > 0:
    results_table_py = getAnnGenes(results_table_py, GO2gene['reactome_pathways'], rank_copy)
    _, df = plotGenesInTerm(results = results_table_py, 
                            GO2gene = GO2gene['reactome_pathways'], DEGs = rank_copy, n_top_terms = 10, cmap = cmap_up)

Code

if len(results_table_py) > 0:
    intTable(df, folder = folder, fileName = 'genesInTerm_Reactome_up.xlsx', save = True)

4.1.0.2 KEGG

Code

curated = msigdb[msigdb['collection'].isin(['kegg_pathways'])]
curated = curated[~curated.duplicated(['geneset', 'genesymbol'])]

aggregated = curated[["geneset", "genesymbol"]].groupby("geneset").count().rename(columns={"genesymbol": "gene_count"})
curated = curated[~curated.geneset.isin(aggregated[aggregated.gene_count > 200].index.tolist())].copy()
curated = curated[~curated.geneset.isin(aggregated[aggregated.gene_count < 15].index.tolist())].copy()
results_table_py = run_ora_catchErrors(mat=rank.T, net=curated, source='geneset', target='genesymbol', verbose=False, n_up=len(rank), n_bottom=0)

Code

intTable(results_table_py, folder = folder, fileName = 'KEGG_up.xlsx', save = True)

Code

if len(results_table_py) > 0:
    results_table_py = getAnnGenes(results_table_py, GO2gene['kegg_pathways'], rank_copy)
    _, df = plotGenesInTerm(results_table_py, GO2gene['kegg_pathways'], rank_copy, n_top_terms = 10, n_top_genes = 15, cmap = cmap_up)

Code

if len(results_table_py) > 0:
    intTable(df, folder = folder, fileName = 'genesInTerm_KEGG_up.xlsx', save = True)

4.2 Downregulated

Code

%%R -i allDownSelected -i allGenes

allGenes_v <- c(allDownSelected)
#print(allGenes_v)
names(allGenes_v) <- allGenes
allGenes_v <- unlist(allGenes_v)

geneNames <- c(allGenes)

ann_org_BP <- topGO::annFUN.org(whichOnto='BP', feasibleGenes=names(allGenes_v), 
                           mapping='org.Hs.eg', ID='symbol')

ann_org_MF <- topGO::annFUN.org(whichOnto='MF', feasibleGenes=names(allGenes_v), 
                           mapping='org.Hs.eg', ID='symbol')

ann_org_CC <- topGO::annFUN.org(whichOnto='CC', feasibleGenes=names(allGenes_v), 
                           mapping='org.Hs.eg', ID='symbol')

selection <- function(allScores){return (as.logical(allScores))}

Biological Process

Code

%%R
#print(lapply(ann_org_BP, count_genes))

GOdata <- new("topGOdata",
  ontology="BP",
  allGenes=allGenes_v,
  annot=annFUN.GO2genes,
  GO2genes=ann_org_BP,
  geneSel = selection,
  nodeSize=10)

Code

%%R -o results

results <- runTest(GOdata, algorithm="weight01",statistic="fisher")

Code

scores = ro.r.score(results)
score_names = ro.r(
'''
names(results@score)
'''
)
go_data = ro.r.GOdata

genesData = ro.r(
'''
geneData(results)
'''
)
genesData

array([13071,   823,    10,  5148], dtype=int32)

Code

#num_summarize = min(100, len(score_names))
results_table = ro.r.GenTable(go_data, weight=results,
        orderBy="weight", topNodes=len(scores))

Code

results_table_py = ro.conversion.rpy2py(results_table)

Code

scores_py = ro.conversion.rpy2py(scores)
score_names = [i for i in score_names]

Code

scores_df = pd.DataFrame({'Scores': scores_py, 'GO.ID': score_names})
results_table_py = results_table_py.merge(scores_df, left_on = 'GO.ID', right_on = 'GO.ID')
results_table_py

	GO.ID	Term	Annotated	Significant	Expected	weight	Scores
0	GO:0030198	extracellular matrix organization	267	77	16.81	5.5e-18	5.486428e-18
1	GO:0009952	anterior/posterior pattern specification	185	45	11.65	2.9e-14	2.875857e-14
2	GO:0045944	positive regulation of transcription by ...	1054	130	66.36	2.1e-13	2.109330e-13
3	GO:0001525	angiogenesis	450	106	28.33	1.1e-12	1.134388e-12
4	GO:0007155	cell adhesion	1176	176	74.05	6.1e-12	6.108184e-12
...	...	...	...	...	...	...	...
6269	GO:2001140	positive regulation of phospholipid tran...	11	0	0.69	1.00000	1.000000e+00
6270	GO:2001185	regulation of CD8-positive, alpha-beta T...	10	0	0.63	1.00000	1.000000e+00
6271	GO:2001252	positive regulation of chromosome organi...	96	0	6.04	1.00000	1.000000e+00
6272	GO:2001256	regulation of store-operated calcium ent...	14	0	0.88	1.00000	1.000000e+00
6273	GO:2001267	regulation of cysteine-type endopeptidas...	14	0	0.88	1.00000	1.000000e+00

6274 rows × 7 columns

Code

results_table_py = results_table_py[results_table_py['Scores'] < 0.05]
results_table_py = results_table_py[results_table_py['Annotated'] < 200]
results_table_py = results_table_py[results_table_py['Annotated'] > 15]

Code

results_table_py['-log10(pvalue)'] = - np.log10(results_table_py.Scores)
results_table_py['Significant/Annotated'] = results_table_py['Significant'] / results_table_py['Annotated']

Code

intTable(results_table_py, folder = folder, fileName = 'GO_BP_down.xlsx', save = True)

Code

%%R
Res <- GenTable(GOdata, weight=results,
        orderBy="weight", topNodes=length(score(results)))
#print(Res[0:10,])
colnames(Res) <- c("GO.ID", "Term", "Annotated", "Significant", "Expected", "Statistics")
Res$ER <- Res$Significant / Res$Expected
bubbleplot(Res, Ont = 'BP', fillCol = 'blue')

Code

%%R -i markers
plotGenesInTerm_v1(Res, GOdata, SE = markers, nterms=15, ngenes=12,
                             fillCol='blue', log = TRUE)

Code

%%R -i markers -i folder
saveGenesInTerm(Res, GOdata, nterms = 20, path = paste0(folder,'GO_BP_genesInTerm_down.xlsx'), SE = markers)

Molecular Function

Code

%%R

GOdata <- new("topGOdata",
  ontology="MF",
  allGenes=allGenes_v,
  annot=annFUN.GO2genes,
  GO2genes=ann_org_MF,
  geneSel = selection,
  nodeSize=10)

Code

%%R -o results

results <- runTest(GOdata, algorithm="weight01",statistic="fisher")

Code

scores = ro.r.score(results)
score_names = ro.r(
'''
names(results@score)
'''
)
go_data = ro.r.GOdata

genesData = ro.r(
'''
geneData(results)
'''
)
genesData

array([13424,   835,    10,   825], dtype=int32)

Code

#num_summarize = min(100, len(score_names))
results_table = ro.r.GenTable(go_data, weight=results,
        orderBy="weight", topNodes=len(scores))

Code

results_table_py = ro.conversion.rpy2py(results_table)

Code

scores_py = ro.conversion.rpy2py(scores)
score_names = [i for i in score_names]

Code

scores_df = pd.DataFrame({'Scores': scores_py, 'GO.ID': score_names})
results_table_py = results_table_py.merge(scores_df, left_on = 'GO.ID', right_on = 'GO.ID')
results_table_py = results_table_py[results_table_py['Scores'] < 0.05]
results_table_py = results_table_py[results_table_py['Annotated'] < 200]
results_table_py = results_table_py[results_table_py['Annotated'] > 15]

intTable(results_table_py, folder = folder, fileName = 'GO_MF_down.xlsx', save = True)

Code

%%R
Res <- GenTable(GOdata, weight=results,
        orderBy="weight", topNodes=length(score(results)))
#print(Res[0:10,])
colnames(Res) <- c("GO.ID", "Term", "Annotated", "Significant", "Expected", "Statistics")
Res$ER <- Res$Significant / Res$Expected
bubbleplot(Res, Ont = 'MF', fillCol = 'blue')

Code

%%R -i markers
plotGenesInTerm_v1(Res, GOdata, SE = markers, nterms=15, ngenes=12,
                             fillCol='blue', log = TRUE)

Code

%%R -i markers -i folder
saveGenesInTerm(Res, GOdata, nterms = 20, path = paste0(folder,'GO_MF_genesInTerm_down.xlsx'), SE = markers)

Cellular Compartment

Code

%%R

GOdata <- new("topGOdata",
  ontology="CC",
  allGenes=allGenes_v,
  annot=annFUN.GO2genes,
  GO2genes=ann_org_CC,
  geneSel = selection,
  nodeSize=10)

Code

%%R -o results

results <- runTest(GOdata, algorithm="weight01",statistic="fisher")

Code

scores = ro.r.score(results)
score_names = ro.r(
'''
names(results@score)
'''
)
go_data = ro.r.GOdata

genesData = ro.r(
'''
geneData(results)
'''
)
genesData

array([13666,   851,    10,   542], dtype=int32)

Code

#num_summarize = min(100, len(score_names))
results_table = ro.r.GenTable(go_data, weight=results,
        orderBy="weight", topNodes=len(scores))

Code

results_table_py = ro.conversion.rpy2py(results_table)

Code

scores_py = ro.conversion.rpy2py(scores)
score_names = [i for i in score_names]

Code

scores_df = pd.DataFrame({'Scores': scores_py, 'GO.ID': score_names})
results_table_py = results_table_py.merge(scores_df, left_on = 'GO.ID', right_on = 'GO.ID')

Code

results_table_py = results_table_py[results_table_py['Scores'] < 0.05]
results_table_py = results_table_py[results_table_py['Annotated'] < 200]
results_table_py = results_table_py[results_table_py['Annotated'] > 15]

intTable(results_table_py, folder = folder, fileName = 'GO_CC_down.xlsx', save = True)

Code

%%R
Res <- GenTable(GOdata, weight=results,
        orderBy="weight", topNodes=length(score(results)))
#print(Res[0:10,])
colnames(Res) <- c("GO.ID", "Term", "Annotated", "Significant", "Expected", "Statistics")
Res$ER <- Res$Significant / Res$Expected
bubbleplot(Res, Ont = 'CC', fillCol = 'blue')

Code

%%R -i markers
plotGenesInTerm_v1(Res, GOdata, SE = markers, nterms=12, ngenes=12,
                             fillCol='blue', log = TRUE)

Code

%%R -i markers -i folder
saveGenesInTerm(Res, GOdata, nterms = 20, path = paste0(folder,'GO_CC_genesInTerm_down.xlsx'), SE = markers)

Pathway annotation

4.2.0.1 Reactome

Code

curated = msigdb[msigdb['collection'].isin(['reactome_pathways'])]
curated = curated[~curated.duplicated(['geneset', 'genesymbol'])]

aggregated = curated[["geneset", "genesymbol"]].groupby("geneset").count().rename(columns={"genesymbol": "gene_count"})
curated = curated[~curated.geneset.isin(aggregated[aggregated.gene_count > 200].index.tolist())].copy()
curated = curated[~curated.geneset.isin(aggregated[aggregated.gene_count < 15].index.tolist())].copy()

Code

rank = pd.DataFrame(markers['-log10(FDR)'])

rank_copy = rank.copy()
rank_copy['pval'] = markers.loc[rank.index].FDR

Code

rank_copy

	-log10(FDR)	pval
names
AASS	244.224107	5.968879e-245
ZIC5	240.682996	2.074933e-241
SALL2	234.699636	1.996935e-235
COX7C	195.275389	5.304089e-196
IGF2BP3	192.633009	2.328046e-193
...	...	...
COL1A1	163.134943	7.329214e-164
COL6A2	174.232475	5.854977e-175
RHOB	176.263020	5.457323e-177
S100A10	187.319065	4.796613e-188
VIM	190.301033	4.999969e-191

7436 rows × 2 columns

Code

results_table_py = run_ora_catchErrors(mat=rank.T, net=curated, source='geneset', target='genesymbol', verbose=False, n_up=len(rank), n_bottom=0)
len(results_table_py)

903

Code

intTable(results_table_py, folder = folder, fileName = 'Reactome_down.xlsx', save = True)

Code

if len(results_table_py) > 0:
    results_table_py = getAnnGenes(results_table_py, GO2gene['reactome_pathways'], rank_copy)
    _, df = plotGenesInTerm(results = results_table_py, GO2gene = GO2gene['reactome_pathways'], DEGs = rank_copy, n_top_terms = 10, cmap = cmap_down)

Code

if len(results_table_py) > 0:
    intTable(df, folder = folder, fileName = 'genesInTerm_Reactome_down.xlsx', save = True)

4.2.0.2 KEGG

Code

curated = msigdb[msigdb['collection'].isin(['kegg_pathways'])]
curated = curated[~curated.duplicated(['geneset', 'genesymbol'])]

aggregated = curated[["geneset", "genesymbol"]].groupby("geneset").count().rename(columns={"genesymbol": "gene_count"})
curated = curated[~curated.geneset.isin(aggregated[aggregated.gene_count > 200].index.tolist())].copy()
curated = curated[~curated.geneset.isin(aggregated[aggregated.gene_count < 15].index.tolist())].copy()

Code

results_table_py = run_ora_catchErrors(mat=rank.T, net=curated, source='geneset', target='genesymbol', verbose=False, n_up=len(rank), n_bottom=0)

Code

intTable(results_table_py, folder = folder, fileName = 'KEGG_down.xlsx', save = True)

Code

if len(results_table_py) > 0:
    results_table_py = getAnnGenes(results_table_py, GO2gene['kegg_pathways'], rank_copy)
    _, df = plotGenesInTerm(results_table_py, GO2gene['kegg_pathways'], rank_copy, n_top_terms = 10, n_top_genes = 15, cmap = cmap_down)

Code

if len(results_table_py) > 0:
    intTable(df, folder = folder, fileName = 'genesInTerm_KEGG_down.xlsx', save = True)

4.3 All significant

Code

%%R -i allSelected -i allGenes

allGenes_v <- c(allUpSelected)
#print(allGenes_v)
names(allGenes_v) <- allGenes
allGenes_v <- unlist(allGenes_v)

geneNames <- c(allGenes)

ann_org_BP <- topGO::annFUN.org(whichOnto='BP', feasibleGenes=names(allGenes_v), 
                           mapping='org.Hs.eg', ID='symbol')

ann_org_MF <- topGO::annFUN.org(whichOnto='MF', feasibleGenes=names(allGenes_v), 
                           mapping='org.Hs.eg', ID='symbol')

ann_org_CC <- topGO::annFUN.org(whichOnto='CC', feasibleGenes=names(allGenes_v), 
                           mapping='org.Hs.eg', ID='symbol')

selection <- function(allScores){return (as.logical(allScores))}

Biological Process

Code

%%R
#print(lapply(ann_org_BP, count_genes))

GOdata <- new("topGOdata",
  ontology="BP",
  allGenes=allGenes_v,
  annot=annFUN.GO2genes,
  GO2genes=ann_org_BP,
  geneSel = selection,
  nodeSize=10)

Code

%%R -o results

results <- runTest(GOdata, algorithm="weight01",statistic="fisher")

Code

scores = ro.r.score(results)
score_names = ro.r(
'''
names(results@score)
'''
)
go_data = ro.r.GOdata

genesData = ro.r(
'''
geneData(results)
'''
)
genesData

array([13071,   250,    10,  3652], dtype=int32)

Code

#num_summarize = min(100, len(score_names))
results_table = ro.r.GenTable(go_data, weight=results,
        orderBy="weight", topNodes=len(scores))

Code

results_table_py = ro.conversion.rpy2py(results_table)

Code

scores_py = ro.conversion.rpy2py(scores)
score_names = [i for i in score_names]

Code

scores_df = pd.DataFrame({'Scores': scores_py, 'GO.ID': score_names})
results_table_py = results_table_py.merge(scores_df, left_on = 'GO.ID', right_on = 'GO.ID')
results_table_py

	GO.ID	Term	Annotated	Significant	Expected	weight	Scores
0	GO:0051444	negative regulation of ubiquitin-protein...	14	4	0.27	0.00011	0.000112
1	GO:0016338	calcium-independent cell-cell adhesion v...	15	4	0.29	0.00015	0.000151
2	GO:0010001	glial cell differentiation	184	9	3.52	0.00022	0.000221
3	GO:0009410	response to xenobiotic stimulus	328	16	6.27	0.00048	0.000485
4	GO:0019637	organophosphate metabolic process	860	19	16.45	0.00102	0.001020
...	...	...	...	...	...	...	...
6269	GO:2001239	regulation of extrinsic apoptotic signal...	36	0	0.69	1.00000	1.000000
6270	GO:2001240	negative regulation of extrinsic apoptot...	26	0	0.50	1.00000	1.000000
6271	GO:2001241	positive regulation of extrinsic apoptot...	10	0	0.19	1.00000	1.000000
6272	GO:2001258	negative regulation of cation channel ac...	36	0	0.69	1.00000	1.000000
6273	GO:2001267	regulation of cysteine-type endopeptidas...	14	0	0.27	1.00000	1.000000

6274 rows × 7 columns

Code

results_table_py = results_table_py[results_table_py['Scores'] < 0.05]
results_table_py = results_table_py[results_table_py['Annotated'] < 200]
results_table_py = results_table_py[results_table_py['Annotated'] > 15]

Code

results_table_py['-log10(pvalue)'] = - np.log10(results_table_py.Scores)
results_table_py['Significant/Annotated'] = results_table_py['Significant'] / results_table_py['Annotated']

Code

intTable(results_table_py, folder = folder, fileName = 'GO_BP_all.xlsx', save = True)

Code

%%R
Res <- GenTable(GOdata, weight=results,
        orderBy="weight", topNodes=length(score(results)))
#print(Res[0:10,])
colnames(Res) <- c("GO.ID", "Term", "Annotated", "Significant", "Expected", "Statistics")
Res$ER <- Res$Significant / Res$Expected
bubbleplot(Res, Ont = 'BP', fillCol = 'forestgreen')

Code

%%R -i markers
plotGenesInTerm_v1(Res, GOdata, SE = markers, nterms=15, ngenes=12,
                             fillCol='forestgreen', log = TRUE)

Code

%%R -i markers -i folder
saveGenesInTerm(Res, GOdata, nterms = 20, path = paste0(folder,'GO_BP_genesInTerm_all.xlsx'), SE = markers)

Molecular Function

Code

%%R

GOdata <- new("topGOdata",
  ontology="MF",
  allGenes=allGenes_v,
  annot=annFUN.GO2genes,
  GO2genes=ann_org_MF,
  geneSel = selection,
  nodeSize=10)

Code

%%R -o results

results <- runTest(GOdata, algorithm="weight01",statistic="fisher")

Code

scores = ro.r.score(results)
score_names = ro.r(
'''
names(results@score)
'''
)
go_data = ro.r.GOdata

genesData = ro.r(
'''
geneData(results)
'''
)
genesData

array([13424,   254,    10,   549], dtype=int32)

Code

#num_summarize = min(100, len(score_names))
results_table = ro.r.GenTable(go_data, weight=results,
        orderBy="weight", topNodes=len(scores))

Code

results_table_py = ro.conversion.rpy2py(results_table)

Code

scores_py = ro.conversion.rpy2py(scores)
score_names = [i for i in score_names]

Code

scores_df = pd.DataFrame({'Scores': scores_py, 'GO.ID': score_names})
results_table_py = results_table_py.merge(scores_df, left_on = 'GO.ID', right_on = 'GO.ID')
results_table_py = results_table_py[results_table_py['Scores'] < 0.05]
results_table_py = results_table_py[results_table_py['Annotated'] < 200]
results_table_py = results_table_py[results_table_py['Annotated'] > 15]

intTable(results_table_py, folder = folder, fileName = 'GO_MF_all.xlsx', save = True)

Code

%%R
Res <- GenTable(GOdata, weight=results,
        orderBy="weight", topNodes=length(score(results)))
#print(Res[0:10,])
colnames(Res) <- c("GO.ID", "Term", "Annotated", "Significant", "Expected", "Statistics")
Res$ER <- Res$Significant / Res$Expected
bubbleplot(Res, Ont = 'MF', fillCol = 'forestgreen')

Code

%%R -i markers
plotGenesInTerm_v1(Res, GOdata, SE = markers, nterms=15, ngenes=12,
                             fillCol='forestgreen', log = TRUE)

Code

%%R -i markers -i folder
saveGenesInTerm(Res, GOdata, nterms = 20, path = paste0(folder,'GO_MF_genesInTerm_all.xlsx'), SE = markers)

Cellular Compartment

Code

%%R

GOdata <- new("topGOdata",
  ontology="CC",
  allGenes=allGenes_v,
  annot=annFUN.GO2genes,
  GO2genes=ann_org_CC,
  geneSel = selection,
  nodeSize=10)

Code

%%R -o results

results <- runTest(GOdata, algorithm="weight01",statistic="fisher")

Code

scores = ro.r.score(results)
score_names = ro.r(
'''
names(results@score)
'''
)
go_data = ro.r.GOdata

genesData = ro.r(
'''
geneData(results)
'''
)
genesData

array([13666,   260,    10,   433], dtype=int32)

Code

#num_summarize = min(100, len(score_names))
results_table = ro.r.GenTable(go_data, weight=results,
        orderBy="weight", topNodes=len(scores))

Code

results_table_py = ro.conversion.rpy2py(results_table)

Code

scores_py = ro.conversion.rpy2py(scores)
score_names = [i for i in score_names]

Code

scores_df = pd.DataFrame({'Scores': scores_py, 'GO.ID': score_names})
results_table_py = results_table_py.merge(scores_df, left_on = 'GO.ID', right_on = 'GO.ID')

Code

results_table_py = results_table_py[results_table_py['Scores'] < 0.05]
results_table_py = results_table_py[results_table_py['Annotated'] < 200]
results_table_py = results_table_py[results_table_py['Annotated'] > 15]

intTable(results_table_py, folder = folder, fileName = 'GO_CC_all.xlsx', save = True)

Code

%%R
Res <- GenTable(GOdata, weight=results,
        orderBy="weight", topNodes=length(score(results)))
#print(Res[0:10,])
colnames(Res) <- c("GO.ID", "Term", "Annotated", "Significant", "Expected", "Statistics")
Res$ER <- Res$Significant / Res$Expected
bubbleplot(Res, Ont = 'CC', fillCol = 'forestgreen')

Code

%%R -i markers
plotGenesInTerm_v1(Res, GOdata, SE = markers, nterms=12, ngenes=12,
                             fillCol='forestgreen', log = TRUE)

Code

%%R -i markers -i folder
saveGenesInTerm(Res, GOdata, nterms = 20, path = paste0(folder,'GO_CC_genesInTerm_all.xlsx'), SE = markers)

Pathway annotation

4.3.0.1 Reactome

Code

curated = msigdb[msigdb['collection'].isin(['reactome_pathways'])]
curated = curated[~curated.duplicated(['geneset', 'genesymbol'])]

aggregated = curated[["geneset", "genesymbol"]].groupby("geneset").count().rename(columns={"genesymbol": "gene_count"})
curated = curated[~curated.geneset.isin(aggregated[aggregated.gene_count > 200].index.tolist())].copy()
curated = curated[~curated.geneset.isin(aggregated[aggregated.gene_count < 15].index.tolist())].copy()

Code

rank = pd.DataFrame(markers['-log10(FDR)'])

rank_copy = rank.copy()
rank_copy['pval'] = markers.loc[rank.index].FDR

Code

rank_copy

	-log10(FDR)	pval
names
AASS	244.224107	5.968879e-245
ZIC5	240.682996	2.074933e-241
SALL2	234.699636	1.996935e-235
COX7C	195.275389	5.304089e-196
IGF2BP3	192.633009	2.328046e-193
...	...	...
COL1A1	163.134943	7.329214e-164
COL6A2	174.232475	5.854977e-175
RHOB	176.263020	5.457323e-177
S100A10	187.319065	4.796613e-188
VIM	190.301033	4.999969e-191

7436 rows × 2 columns

Code

results_table_py = run_ora_catchErrors(mat=rank.T, net=curated, source='geneset', target='genesymbol', verbose=False, n_up=len(rank), n_bottom=0)
len(results_table_py)

903

Code

intTable(results_table_py, folder = folder, fileName = 'Reactome_all.xlsx', save = True)

Code

if len(results_table_py) > 0:
    results_table_py = getAnnGenes(results_table_py, GO2gene['reactome_pathways'], rank_copy)
    _, df = plotGenesInTerm(results = results_table_py, GO2gene = GO2gene['reactome_pathways'], DEGs = rank_copy, n_top_terms = 10, cmap = cmap_all)

Code

if len(results_table_py) > 0:
    intTable(df, folder = folder, fileName = 'genesInTerm_Reactome_all.xlsx', save = True)

4.3.0.2 KEGG

Code

curated = msigdb[msigdb['collection'].isin(['kegg_pathways'])]
curated = curated[~curated.duplicated(['geneset', 'genesymbol'])]

aggregated = curated[["geneset", "genesymbol"]].groupby("geneset").count().rename(columns={"genesymbol": "gene_count"})
curated = curated[~curated.geneset.isin(aggregated[aggregated.gene_count > 200].index.tolist())].copy()
curated = curated[~curated.geneset.isin(aggregated[aggregated.gene_count < 15].index.tolist())].copy()

Code

results_table_py = run_ora_catchErrors(mat=rank.T, net=curated, source='geneset', target='genesymbol', verbose=False, n_up=len(rank), n_bottom=0)

Code

intTable(results_table_py, folder = folder, fileName = 'KEGG_all.xlsx', save = True)

Code

if len(results_table_py) > 0:
    results_table_py = getAnnGenes(results_table_py, GO2gene['kegg_pathways'], rank_copy)
    _, df = plotGenesInTerm(results_table_py, GO2gene['kegg_pathways'], rank_copy, n_top_terms = 10, n_top_genes = 15, cmap = cmap_all)

Code

if len(results_table_py) > 0:
    intTable(df, folder = folder, fileName = 'genesInTerm_KEGG_all.xlsx', save = True)